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In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
Font embedding is the inclusion of font files inside an electronic document for display across different platforms.
An embedding, or a smooth embedding, is defined to be an immersion that is an embedding in the topological sense mentioned above (i.e. homeomorphism onto its image). [ 4 ] In other words, the domain of an embedding is diffeomorphic to its image, and in particular the image of an embedding must be a submanifold .
Object Linking and Embedding (OLE) is a proprietary technology developed by Microsoft that allows embedding and linking to documents and other objects. For developers, it brought OLE Control Extension (OCX), a way to develop and use custom user interface elements.
It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier. [1] It has also been used for computer vision. [2]
Re. "Most new word embedding techniques rely on a neural network architecture instead of more traditional n-gram models and unsupervised learning"--Can someone rewrite this to indicate whether it is "rely on (a neural network architecture) instead of (more traditional n-gram models and unsupervised learning)" or "rely on (a neural network architecture (instead of more traditional n-gram models ...
The word "walk" is the base form for the word "walking", and hence this is matched in both stemming and lemmatization. The word "meeting" can be either the base form of a noun or a form of a verb ("to meet") depending on the context; e.g., "in our last meeting" or "We are meeting again tomorrow".
The architecture of vision transformer. An input image is divided into patches, each of which is linearly mapped through a patch embedding layer, before entering a standard Transformer encoder. A vision transformer (ViT) is a transformer designed for computer vision. [1]